- A
Training loss convergence.
Why wrong: Training loss is from training, not production serving.
- B
Prediction distribution (prediction drift).
Changes in prediction distribution can indicate concept drift.
- C
Feature distribution (data drift).
Data drift in input features can cause model degradation.
- D
CPU utilization of the serving nodes.
Why wrong: CPU utilization is an infrastructure metric, not a drift indicator.
- E
Model performance metrics (e.g., accuracy, precision, recall) on a ground truth dataset.
Performance metrics directly measure model accuracy; a decline indicates drift.
Question 984 of 1,000
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE metrics should be monitored to detect model drift in a production ML system?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Prediction distribution (prediction drift).
Prediction distribution (prediction drift) is a key metric for detecting model drift because it monitors changes in the model's output probabilities or class frequencies over time. A significant shift in prediction distribution often indicates that the underlying data relationships have changed, even if feature distributions remain stable. This is a direct signal of model decay and is commonly tracked using statistical tests like the Population Stability Index (PSI) or Kolmogorov-Smirnov (KS) test on the prediction scores.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Training loss convergence.
Why it's wrong here
Training loss is from training, not production serving.
- ✓
Prediction distribution (prediction drift).
Why this is correct
Changes in prediction distribution can indicate concept drift.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Feature distribution (data drift).
Why this is correct
Data drift in input features can cause model degradation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
CPU utilization of the serving nodes.
Why it's wrong here
CPU utilization is an infrastructure metric, not a drift indicator.
- ✓
Model performance metrics (e.g., accuracy, precision, recall) on a ground truth dataset.
Why this is correct
Performance metrics directly measure model accuracy; a decline indicates drift.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is the misconception that training metrics like loss convergence are relevant for production monitoring, when in fact they are only applicable during the training phase and have no role in detecting post-deployment drift.
Detailed technical explanation
How to think about this question
Under the hood, prediction drift detection often involves comparing the distribution of model scores (e.g., softmax outputs for classification) between a reference window (e.g., training data or a recent stable period) and a current window using divergence metrics like KL divergence or PSI. A subtle behavior is that prediction drift can occur even without feature drift if the model's decision boundary has shifted due to concept drift, making it a more sensitive early warning signal. In a real-world scenario, a credit scoring model might show stable feature distributions but a sudden increase in default predictions, indicating that the economic environment has changed and the model's calibration is no longer valid.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prediction distribution (prediction drift). — Prediction distribution (prediction drift) is a key metric for detecting model drift because it monitors changes in the model's output probabilities or class frequencies over time. A significant shift in prediction distribution often indicates that the underlying data relationships have changed, even if feature distributions remain stable. This is a direct signal of model decay and is commonly tracked using statistical tests like the Population Stability Index (PSI) or Kolmogorov-Smirnov (KS) test on the prediction scores.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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